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Reduced-Complexity Approach for Opacity Enforcement Based on Extended Insertion Functions with Limited Insertion Events

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Reduced-Complexity Approach for Opacity Enforcement Based on...

IEEE International Conference on Networking, Sensing and Control

作者： Xiaoyan Li Christoforos N. Hadjicostis Zhiwu Li School of Information and Communication Engineering North University of China Taiyuan China Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Institute of Systems Engineering Macau University of Science and Technology Macau SAR China

Opacity is a security and privacy property that guarantees predefined secret behavior of a cyber-physical system will not be inferred with certainty by a malicious entity. If a system is opacity-violating, extended insertion functions that insert fake symbols can be implemented to enforce opacity. By constricting the ways in which the fake symbols are inserted, this paper addresses opacity enforcement under the framework of finite state automata using extended insertion functions under event insertion constraints and limited insertion events constraints, i.e., only a limited number of symbols in a specified set can be inserted before and after an actual system output. The notion of (k b ,k a )-EIC-enforceability is proposed to describe how to enforce opacity via extended insertion functions in the presence of both event insertion constraints and limited insertion events constraints. A necessary and sufficient condition to check (k b ,k a )-EIC-enforceability is derived by constructing an appropriate verifier.

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CKTGNN: CIRCUIT GRAPH NEURAL NETWORK FOR ELECTRONIC DESIGN AUTOMATION

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arXiv 2023年

作者： Dong, Zehao Cao, Weidong Zhang, Muhan Tao, Dacheng Chen, Yixin Zhang, Xuan Department of Computer Science & Engineering Washington University St. Louis United States Department of Electrical & Systems Engineering Washington University St. Louis United States Institute for Artificial Intelligence Peking University China School of Computer Science The University of Sydney Australia

The electronic design automation of analog circuits has been a longstanding challenge in the integrated circuit field due to the huge design space and complex design trade-offs among circuit specifications. In the past decades, intensive research efforts have mostly been paid to automate the transistor sizing with a given circuit topology. By recognizing the graph nature of circuits, this paper presents a Circuit Graph Neural Network (CktGNN) that simultaneously automates the circuit topology generation and device sizing based on the encoder-dependent optimization subroutines. Particularly, CktGNN encodes circuit graphs using a two-level GNN framework (of nested GNN) where circuits are represented as combinations of subgraphs in a known subgraph basis. In this way, it significantly improves design efficiency by reducing the number of subgraphs to perform message passing. Nonetheless, another critical roadblock to advancing learning-assisted circuit design automation is a lack of public benchmarks to perform canonical assessment and reproducible research. To tackle the challenge, we introduce Open Circuit Benchmark (OCB), an open-sourced dataset that contains 10K distinct operational amplifiers with carefully-extracted circuit specifications. OCB is also equipped with communicative circuit generation and evaluation capabilities such that it can help to generalize CktGNN to design various analog circuits by producing corresponding datasets. Experiments on OCB show the extraordinary advantages of CktGNN through representation-based optimization frameworks over other recent powerful GNN baselines and human experts' manual designs.. Our work paves the way toward a learning-based open-sourced design automation for analog circuits. Our source code is available at https://***/zehao-dong/CktGNN. Copyright © 2023, The Authors. All rights reserved.

关键词： Timing circuits

Performance Evaluation of Peer-to-Peer Distributed Microgrids Coordination for Voltage Regulation

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Performance Evaluation of Peer-to-Peer Distributed Microgrid...

2022 IEEE Power and Energy Society General Meeting, PESGM 2022

作者： Gan, Houchao Wang, Jing Lin, Yashen Bhela, Siddharth Bilby, Chris Power Systems Engineering Center National Renewable Energy Laboratory GoldenCO80401 United States Clarkson University Electrical and Computer Engineering PotsdamNY13699 United States Siemens Technology PrincetonNJ United States Holy Cross Energy Glenwood SpringsCO81601 United States

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408233

This paper presents the performance evaluation of a peer-to-peer microgrids coordination algorithm for sub-transmission systems. As distributed energy resources (DERs) in distribution system start to show negative impact to the bulk power system, a paradigm shift is needed for transmission planning and operation. Because distribution substations are located far from the sub-transmission system, and it is hard to use traditional centralized control for real-time control and coordination. Thus, distributed control is a natural choice because it requires less communication and central computation. In this paper, each distribution substation is treated as a microgrid, and the peer-to-peer distributed microgrids control is formulated as a real-time optimal power flow problem to reduce the negative impact in sub-transmission systems. A distributed primal-dual optimization algorithm is adopted to solve the problem. Validation of the peer-to-peer algorithm is performed through the simulation of a real-world sub-transmission system composing of many distribution systems with high renewable penetration. Simulation results show that the peer-to-peer algorithm can achieve satisfactory voltage regulation performance in sub-transmission system by coordinating and controlling DERs in distribution systems. © 2022 IEEE.

关键词： Real time control

Multimodal Classification and Out-of-distribution Detection for Multimodal Intent Understanding

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arXiv 2024年

作者： Zhang, Hanlei Zhou, Qianrui Xu, Hua Su, Jianhua Evans, Roberto Gao, Kai State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing100084 China School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050018 China

Multimodal intent understanding is a significant research area that requires effectively leveraging multiple modalities to analyze human language. Existing methods face two main challenges in this domain. Firstly, they have limitations in capturing nuanced and high-level semantics underlying complex in-distribution (ID) multimodal intents. Secondly, they exhibit poor generalization when confronted with unseen out-of-distribution (OOD) data in real-world scenarios. To address these issues, we propose a novel method for both ID classification and OOD detection (MIntOOD). We first introduce a weighted feature fusion network that models multimodal representations effectively. This network dynamically learns the importance of each modality, adapting to multimodal contexts. To develop discriminative representations that are conducive to both tasks, we synthesize pseudo-OOD data from convex combinations of ID data and engage in multimodal representation learning from both coarse-grained and fine-grained perspectives. The coarse-grained perspective focuses on distinguishing between ID and OOD binary classes, while the fine-grained perspective enhances the understanding of ID data by incorporating binary confidence scores. These scores help to gauge the difficulty of each sample, improving the classification of different ID classes. Additionally, the fine-grained perspective captures instance-level interactions between ID and OOD samples, promoting proximity among similar instances and separation from dissimilar ones. We establish baselines for three multimodal intent datasets and build an OOD benchmark. Extensive experiments on these datasets demonstrate that our method significantly improves OOD detection performance with a 3-10% increase in AUROC scores while achieving new state-of-the-art results in ID classification. 1 Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

A Supervision Game-Theoretic Incentive Mechanism for Cooperative Sensing in Connected and Autonomous Vehicles

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A Supervision Game-Theoretic Incentive Mechanism for Coopera...

Future Communications and Networks (FCN), International Conference on

作者： Pincan Zhao Changle Li Xinrui Zhang Yuchuan Fu Mengqiu Tian State Key Laboratory of Integrated Services Networks Xidian University Xi’an Shaanxi China School of Information Technology Carleton University Ottawa ON Canada Department of Systems and Computer Engineering Carleton University Ottawa ON Canada

ISBN: (数字)9798331512880

ISBN: (纸本)9798331512897

Cooperative sensing has emerged as a promising approach to enhance the sensing capabilities of connected and autonomous vehicles (CAVs) by enabling them to share sensory data through vehicle-to-vehicle communication. However, the lack of motivation for honest participation and the presence of malicious vehicles hinder the widespread adoption of cooperative sensing. To address these challenges, we propose a novel incentive mechanism based on a supervision-oriented game-theoretic approach. Our framework models the interactions between sensing providers (SPs) and sensing requesters (SRs) as a strategic game, where a supervision mechanism is introduced to monitor and penalize misbehavior. We analyze the Nash equilibrium of the game and derive optimal strategies for both SPs and SRs. Extensive simulations demonstrate the effectiveness of our approach in promoting sensing cooperation, ensuring the utility of participants, and improving the overall performance of cooperative sensing in CAVs compared to existing methods.

关键词： Connected vehicles Biological system modeling Ecosystems Vehicular ad hoc networks Games Nash equilibrium Data models Sensors Autonomous vehicles Monitoring

TEMPORAL UP-SAMPLING for ASYNCHRONOUS EVENTS

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arXiv 2022年

作者： Xiang, Xijie Zhu, Lin Li, Jianing Tian, Yonghong Huang, Tiejun Department of Electrical and Computer Engineering Shanghai Jiao Tong University China School of Computer Science Peking University China Peng Cheng National Laboratory China

The event camera is a novel bio-inspired vision sensor. When the brightness change exceeds the preset threshold, the sensor generates events asynchronously. The number of valid events directly affects the performance of event-based tasks, such as reconstruction, detection, and recognition. However, when in low-brightness or slow-moving scenes, events are often sparse and accompanied by noise, which poses challenges for event-based tasks. To solve these challenges, we propose an event temporal up-sampling algorithm1 to generate more effective and reliable events. The main idea of our algorithm is to generate up-sampling events on the event motion trajectory. First, we estimate the event motion trajectory by contrast maximization algorithm and then up-sampling the events by temporal point processes. Experimental results show that upsampling events can provide more effective information and improve the performance of downstream tasks, such as improving the quality of reconstructed images and increasing the accuracy of object detection. Copyright © 2022, The Authors. All rights reserved.

关键词： Luminance

Raformer: Redundancy-Aware Transformer for Video Wire Inpainting

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arXiv 2024年

作者： Ji, Zhong Su, Yimu Zhang, Yan Hou, Jiacheng Pang, Yanwei Han, Jungong The School of Electrical and Information Engineering Tianjin Key Laboratory of Brain-Inspired Intelligence Technology Tianjin University Tianjin300072 China The Shanghai Artificial Intelligence Laboratory Shanghai200232 China The Department of Computer Science The University of Sheffield United Kingdom

Video Wire Inpainting (VWI) is a prominent application in video inpainting, aimed at flawlessly removing wires in films or TV series, offering significant time and labor savings compared to manual frame-by-frame removal. However, wire removal poses greater challenges due to the wires being longer and slimmer than objects typically targeted in general video inpainting tasks, and often intersecting with people and background objects irregularly, which adds complexity to the inpainting process. Recognizing the limitations posed by existing video wire datasets, which are characterized by their small size, poor quality, and limited variety of scenes, we introduce a new VWI dataset with a novel mask generation strategy, namely Wire Removal Video Dataset 2 (WRV2) and Pseudo Wire-Shaped (PWS) Masks. WRV2 dataset comprises over 4,000 videos with an average length of 80 frames, designed to facilitate the development and efficacy of inpainting models. Building upon this, our research proposes the Redundancy-Aware Transformer (Raformer) method that addresses the unique challenges of wire removal in video inpainting. Unlike conventional approaches that indiscriminately process all frame patches, Raformer employs a novel strategy to selectively bypass redundant parts, such as static background segments devoid of valuable information for inpainting. At the core of Raformer is the Redundancy-Aware Attention (RAA) module, which isolates and accentuates essential content through a coarse-grained, window-based attention mechanism. This is complemented by a Soft Feature Alignment (SFA) module, which refines these features and achieves end-to-end feature alignment. Extensive experiments on both the traditional video inpainting datasets and our proposed WRV2 dataset demonstrate that Raformer outperforms other state-of-the-art methods. Our codes and the WRV2 dataset will be made available at: https://***/Suyimu/WRV2. © 2024, CC BY.

关键词： Wire

Multi-Granularity Prosodic Speech Synthesis with Grammar Information

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SSRN

SSRN 2024年

作者： Zhou, Jian Liu, Jiahui Fan, Cunhang Zheng, Wenming Lv, Zhao Tao, Liang Kwan, Hon Keung The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China Ministry of Education School of Biological Science and Medical Engineering Southeast University Nanjing210096 China The Department of Electrical and Computer Engineering University of Windsor WindsorN9B 3P4 Canada

Personalized speech synthesis involves learning an individual's unique speaking patterns from a reference audio to generate speech that mimics the speaker's habitual rhythm. However, the one-to-many mapping between text and speech, combined with the dynamic nature of personalized speech style characteristics in the reference audio, poses significant challenges for accurate personalized speech synthesis. In this paper, we propose a Grammar Infused Multi-granularity Prosody Network (GMG ProsodyNet) for personalized speech synthesis. Specifically, 1) We model prosodic features hierarchically at the levels of utterance, content syntax, word, and phoneme. We utilize the utterance-level prosodic feature to guide the prediction of fine-grained prosodic features according to the hierarchical property of prosodic features. We introduce a context syntax encoder to improve the prediction accuracy of duration, pitch, and energy characteristics in the synthesized audio. The proposed word-level prosodic encoder can efficiently extract valuable pitch dynamic and speech continuity features from the word spectrum. For controlling subtle prosodic nuances, we employ phoneme-level prosodic modeling. 2) We extract syntax level prosodic features from the syntax graph constructed by Graph Neural Network (GNN) with word as node, exploiting grammatical dependencies between distant words. Experimental results demonstrate that the proposed GMG ProsodyNet can effectively encode delicate and personalized prosodic features, leading to improved speech synthesis quality, fluency, and naturalness. © 2024, The Authors. All rights reserved.

关键词： Speech synthesis

Heart Failure: Machine Learning Prediction Within a 5-Year Framework 24

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Heart Failure: Machine Learning Prediction Within a 5-Year F...

24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Tsarapatsani, Konstantina-Helen Tsakanikas, Vasilios D. Sakellarios, Antonis Trampisch, Hans J. Karapintzou, Efterpi Rudolf, Henrik Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of Ioannina and Biomedical Research Institute-FORTH Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Ioannina Greece

ISBN: (纸本)9798331518622

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction. © 2024 IEEE.

关键词： Risk perception